STUDY HOW ENVIRONMENTAL AND PSYCHOLOGICAL FACTORS AFFECT SICK BUILDING SYNDROME (SBS) AND PRODUCTIVITY

Abstract

This study aims to develop predictive models to better manage issues such as declining productivity gains and increasing signs of Sick Building Syndrome that affect the economy. The 4 models (Y_1 to Y_4) included factors that were extracted from the Principal Component Analyses to remove multicollinearity issues within variables (environmental and psychological factors that were obtained with the physical measurements, surveys and productivity tests of the Green and Non-Green offices).

Model 1: Overall satisfaction of physical indoor environment (Y_1) = 73.71 – 238.46 Indoor HCHO + 18.10 “Satisfaction of thermal and job satisfaction” +14.02 “Satisfaction of acoustic” + 9.36 “Satisfaction of visual performance and air quality” + 9.25 “Physical biological and thermal parameters” + 7.02 “Satisfaction of level of control”+ 5.17 “Stress, Effort required & Difficulty” – 0.12 “Now” Importance of Personal Control – 0.08 Indoor CO2.

Model 2: Number of SBS Symptoms (Y_2) = 1.82 – 0.710 “Satisfaction of thermal and job satisfaction” – 0.60 “Satisfaction of acoustic” – 0.55 “Satisfaction of level of control” – 0.49 “Physical air quality (ventilation and pollutant) & lighting”– 0.47 “Satisfaction of visual performance and air quality” + 0.02 “Now” Importance of Personal Control.

Model 3: Concentration Score (Y_3) = – 13.01 – 67.30 Indoor HCHO – 2.04 “Physical air quality (ventilation and pollutant) & lighting” – 0.35 Indoor RH – 0.07 Fungi (AV).

Model 4: Working Memory Score (Y_4) = 10.77 – 0.55 “Physical biological and operative temperature” + 0.02 “Now” Importance of Personal Control.

The study provides further evidence of the impact of formaldehyde with models 1 and 3. Both the perceived influence and model 3 and 4 emphasised on the impact of environmental parameters on productivity. It also reveals the importance of providing adequate type of personal controls with the correlation with Y_1 and Y_2. The inclusion of many psychological parameters in predicting Y_1 and Y_2 suggests the importance of ensuring good mental health and taking measures to avoid dissatisfaction and stress.

With the ability to predict overall satisfaction of the environment, SBS issues and productivity, it aids organisations to tackle the problem of low productivity and health issues. Model 1 is able to explain 62.3% of the variation in Y_1, followed by model 2 with 28.6%, model 3 with 11.5% and lastly model 4 with only 5.7% explained. As most of the environmental parameters fell within the guideline values, hence, they will pose little effect on Y_1 to Y_4. This might hinder the actual relationship. In addition, despite the results suggest that Non-Green offices provided a better environment, the occupants of the Green offices were more satisfied with the environment. Thus, further investigation is needed.